class_docstr: A factory class for building a TermDocMatrix from a scikit-learn-processed
  dataset.
  
  >>> from scattertext import TermDocMatrixFromScikit
  >>> from sklearn.datasets import fetch_20newsgroups
  >>> from sklearn.feature_extraction.text import CountVectorizer
  >>> newsgroups_train = fetch_20newsgroups(subset='train', remove=('headers', 'footers', 'quotes'))
  >>> count_vectorizer = CountVectorizer()
  >>> X_counts = count_vectorizer.fit_transform(newsgroups_train.data)
  >>> term_doc_mat = TermDocMatrixFromScikit(
  ...   X = X_counts,
  ...   y = newsgroups_train.target,
  ...   feature_vocabulary=count_vectorizer.vocabulary_,
  ...   category_names=newsgroups_train.target_names
  ... ).build()
  >>> term_doc_mat.get_categories()[:2]
  ['alt.atheism', 'comp.graphics']
  >>> term_doc_mat.get_term_freq_df().assign(score=term_doc_mat.get_scaled_f_scores('alt.atheism')).sort_values(by='score', ascending=False).index.tolist()[:5]
  ['atheism', 'atheists', 'islam', 'atheist', 'matthew']
code: def _get_build_kwargs(self):
      constructor_kwargs = {
          "X": self.X,
          "mX": csr_matrix((0, 0)),
          "y": self.y,
          "term_idx_store": IndexStoreFromDict.build(
              self.feature_vocabulary
          ),
          "metadata_idx_store": IndexStore(),
          "category_idx_store": IndexStoreFromList.build(
              self.category_names
          ),
          "unigram_frequency_path": self.unigram_frequency_path,
      }
      return constructor_kwargs
  
file_path: scattertext/scattertext/TermDocMatrixFromScikit.py
imports: from scattertext.TermDocMatrix import TermDocMatrix
  from scattertext.indexstore import IndexStore, IndexStoreFromList, IndexStoreFromDict
  from scipy.sparse import csr_matrix
label_desc: Use image node_id 3 for calling the TermDocMatrixFromScikit obj's underlying member method code with example usage: obj._get_build_kwargs() and returns: constructor_kwargs